Sixth International Conference on Spoken Language Processing
This paper describes the use of a weighted mixture of classbased n-gram language models to perform topic adaptation. By using a fixed class n-gram history and variable word-given-class probabilities we obtain large improvements in the performance of the class-based language model, giving it similar accuracy to a word n-gram model, and an associated small but statistically significant improvement when we interpolate with a word-based n-gram language model.
Bibliographic reference. Moore, Gareth / Young, Steve (2000): "Class-based language model adaptation using mixtures of word-class weights", In ICSLP-2000, vol.4, 512-515.